FIT3155 - Advanced data structures and algorithms

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Overview

This unit builds on the concepts learnt in introductory algorithms and data structures study. It covers advanced algorithmic paradigms and problem-solving techniques required to address real-world programming challenges. It explores, in depth, the design and analysis of space-efficient data structures and time-efficient problem solving strategies to be used with them. … For more content click the Read More button below.

This unit builds on the concepts learnt in introductory algorithms and data structures study. It covers advanced algorithmic paradigms and problem-solving techniques required to address real-world programming challenges. It explores, in depth, the design and analysis of space-efficient data structures and time-efficient problem solving strategies to be used with them. Topics include amortized analysis, advanced sorting and searching algorithms, new tree/string/graph data structures and algorithms, and number-theoretic algorithms amongst others.

Offerings

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Dr Arun Konagurthu

Unit Coordinator(s)

Associate Professor Anuja Dharmaratne

Notes

Labs are scheduled from week 2 to week 12.

Learning outcomes

On successful completion of this unit, you should be able to:

Analyse efficient data structures and effective algorithmic paradigms;

Design and implement efficient algorithms and data structures for use on large data sets;

Apply advanced algorithms and data structures to tackle complex computational problems;

Prove the correctness of programs and reason about their space and time complexities.

Teaching approach

Active learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assignment 1

2 - Assignment 2

3 - Assignment 3

4 - Scheduled final assessment (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Learning resources

Technology resources

Availability in areas of study

Advanced computer science
Computational science